Balaji Subramanian

The problem of secure resource allocation in cooperative Non-Orthogonal Multiple Access (NOMA) network is difficult due to the secrecy-aware power control. The essential parameters like relay cooperation, inter-user interference, relay selection and the risk of eavesdropping in a highly non-convex optimization space optimize the resource allocation. In this paper, a single secrecy-conscious power-allocation scheme of relay-assisted downlink NOMA with passive eavesdropping, cooperative jamming, Decode-and-Forward (DF) and Amplify-and-Forward (AF) relaying are formulated. The objective is to maximize the effective secrecy throughput sum of the legitimate users through joint optimization of source transmit power and relay transmit power. The influence of optimization of source and relay transmit powers on jamming, codeword rate, and secrecy rate under practical communication constraints are also explored. Harris Hawks Optimization (HHO) algorithm is proposed as the numerical optimizer in this work. The proposed HHO combines the exploration and exploitation capabilities of the standard HHO with additional refinement strategies to improve convergence speed, solution stability, and optimization accuracy for secrecy-aware resource allocation. The analysis also bring out the role of the transmit power, the density of the relays, the strength of jamming, the rate of the codeword, and the rate of secrecy on the reliability-confidentiality trade-off. Besides performance gains, an enhancement of secrecy sum of about 6% in DF is obtained over AF when comparing the proposed HHO with Genetic Algorithm (GA). Also, around 17% improvement in computational run time is achieved with respect to GA. The performance of proposed HHO is validated using p-value, and 95% confidence interval statistical tests. The results indicate that the proposed HHO-based framework provides an effective and computationally manageable solution for secrecy-aware cooperative NOMA resource allocation problems.

This work introduces a metaheuristic (MH) optimization method, which is inspired by the red-tailed hawks' predatory behavior is Improved Red-tailed Hawk (IRTHA) Algorithm. The algorithm uses a dynamic adjustment method which uses the combined effect of nonlinear decay and chaotic mapping to enhance the convergence efficacy and accuracy of outcomes. This enhancement affects the search radius of the algorithm and creates diversity in the dive speed of hawks, hence adaptively balancing exploration and exploitation, enhancing diversity and convergence. IRTHA's efficacy is examined for single, double, and triple diode models of various photovoltaic (PV) cells and modules, such as RTC France, PVM 752, STP 120/36, STM 40/36, and Photowatt-PWP201. A comparative analysis of IRTHA with other advanced MH optimization techniques indicates that IRTHA exhibits considerably lower RMSE values: 7.72986E-04 for SDM-RTC France, 7.41918E-04 for DDM-RTC France, 7.34782E-04 for TDM-RTC France, 1.59243E-04 for PVM 752, 1.44508E-02 for STP 120/36, 1.72192E-03 for STM 40/36, and 2.05285E-03 for the Photowatt-PWP201 module, respectively. The reliability of IRTHA is futher validated by statistical analyses, including non-parametric tests (Friedman and Wilcoxon rank-sum tests), convergence curve assessments, and graphical representations with boxplots, which collectively confirm its potential to deliver robust and computationally efficient optimization. From the outcomes, it is observed that the IRTHA demonstrates superior performance compared to other existing MH algorithms. The results obtained by IRTHA show exceptional performance in PV system modeling and parameter estimation in solar PV applications.

The evolution of renewable energy-based power systems and smart grids has posed challenges in the development of innovative power conversion systems. The fluctuating load demands coupled with the intermittent nature of RES necessitate the combination of two or more energy sources along with auxiliary storage systems. This integration is crucial to meet load demands, enhance the system’s dynamic, steady-state characteristics, consistency, and availability. MPC DC-DC converters are required to facilitate the integration of RES with an ESS. These converters enable effective power flow control between RES, battery ports, and load ports in distributed power generation systems. This article comprehensively examines several topologies of MPCs associated with RES, ESS, EVs, and DC microgrids. This paper also provides an in-depth investigation, comparisons, and evaluations of non-isolated, isolated, and partially isolated MPC. It examines the diverse topologies relevant to each type of isolation, emphasizing the recent advancement of MPCs. A unique, extensive comparative analysis is conducted on the converter architectures and features, which includes operating conditions, device count, efficiency, control strategy, prototype rating, power loss analysis, switching voltage stress, voltage gain, and number of ports. Additionally, an investigation of the performance on different MPCs is carried out, thereby identifying the benefits and shortcomings of the different topologies, which are recommended for future research.

The objective of the paper is to exhibit the significance of the LiFi-based data transmission using light. LiFi is deployed in numerous applications such as security, augmented reality, intelligent transport system etc as typical indoor localization is essential and is being done with mobile robots. This research article proposes a short range, indoor design, light fidelity model and discusses the simulations conducted for the Lifi model and the model is analyzed for various aspects that uses different LED light sources with Line Of Sight (LOS) and without Non Light Of Sight (NLOS), different room sizes, different modulation formats, and simulation is also performed with and without noise models. The LiFi proposed model is designed to transfer data wirelessly with a data rate of 10 Gbps. The proposed results obtained is validated with other pervious Lifi works. Simulations results of our proposed model outperform the other previous Lifi works when operating under ideal circumstances with and without noise.

With the rapid increase in IoT devices and advanced machine learning and deep learning techniques, there has been a growing concern about computational cost and data privacy issues since the data coming from IoT devices is non-independent identically distributed (non-IID). However, the implementation of the federated learning algorithm has proven to be a booster in the performance and a solution to the existing data privacy concerns. This paper gives insight into topics such as Blockchain, Unmanned Aerial Vehicles (UAV), Wireless communication, Vehicular Internet of Things, Healthcare, and Cloud Computing and how they have been implemented and co-related to federated Learning and the application and the emerging use cases in the field of federated learning (FL) with respect to the above-mentioned topics have also been discussed. This paper uniquely shows how federated learning has an edge over the traditional machine learning and deep learning techniques in IoT infrastructure since computing nodes are trained using local models on the devices and then these local models are uploaded to the central global server instead of data directly into a global model on a central server ensuring data privacy.

To fulfill fast-growing energy needs, all energy sources should be utilized. Renewable energy is infinite and clean. However, its main disadvantage is that renewable energy sources are intermittent. A Hybrid Renewable Energy System (HRES) is built by integrating several distinct energy sources to deal with this problem. In regards to energy economy, economics, dependability, and flexibility, these hybrid systems can surpass the limits of individual energy producing technologies. The power capacity of HRESs increased from 700 GW to 3100 GW globally over the period 2000–2021. This study aimed to offer and analyze a comprehensive literature review of recently published works by several researchers in the area of HRESs. The HRES contains different Hybrid Energy Systems (HESs), which are categorized into three parts, namely, PV_Other, Wind_Other and PV_Wind_Other. These systems, based on different optimization techniques/software with techno-economic objective functions and constraints, are reviewed in this paper. The optimal sizing, control, and power management strategies of the HRES are elaborately discussed to harness its potential. It has been determined that Metaheuristic (MH) methods and HOMER software are mostly employed in the fields of HRES sizing, control, power management, and optimization. The review provides a critical analysis of the shortcomings of the existing HRES systems, while choosing optimization parameters, and control and power management schemes. Moreover, the study encapsulates the various challenges/barriers in adopting HRESs. Finally, this review highlights possible future opportunities for PV, Wind, and other HESs in the area of control, power management, optimization, and optimal sizing.